As a part of an integrated improved oil recovery project, it was required to evaluate asphaltene formation arising from contacting nitrogen gas with the reservoir fluid. This paper discusses the experimental work associated with the asphaltene evaluation. The subject reservoir was known to have operational problems due to asphaltene precipitation during primary production. Hence, laboratory experiments using the transmittance of an optimized laser light in the near infrared (NIR) wavelength (~1600 nm) were used to first define the pressure-temperature regions of asphaltene instability of the reservoir fluid. Subsequently, several light transmittance experiments were conducted to evaluate the asphaltene instability regions by contacting various molar concentrations (5, 10, & 20%) of nitrogen gas with the reservoir fluid. Also, measurements were conducted to quantify the bulk precipitation of asphaltene with various molar concentrations of nitrogen. Results indicated that nitrogen gas aggravated the asphaltene instability, and also, increased the bulk precipitation amount with increasing concentrations of nitrogen gas in the reservoir fluid. The aggravated asphaltene instability could potentially nullify any expected benefits of improved oil recovery with nitrogen injection as pressure maintenance. Introduction Reservoir A has been under production with primary depletion for more than 20 years. The reservoir performance indicated minimal acqufier support and the reservoir pressure is expected to decline below the saturation pressure soon. Hence, a feasibility study was undertaken to evaluate the potential of improved oil recovery by pressure maintanance using nitrogen. A summary of the reservoir ‘A’ fluid properties is provided in Table 1. These properties confirm a typical black-oil reservoir fluid. Properties of note for the stock tank oil include a n-C7 insoluble asphaltene content of 1.4% (w/w) and a wax content of 1.2% (w/w) (UOP 46–64). The wax content did not appear to raise any concern, as there was no evidence of operational problem in the field related to wax. However, the subject reservoir was known to have operational problems due to asphaltene precipitation during primary production. Hence, it was necessary to evaluate the mechanisms of asphaltene formation arising from contacting nitrogen gas with the reservoir fluid. Asphaltenes are high molecular weight organic fractions of crude oils that are soluble in toluene, but are insoluble in alkanes (n-heptane/n-pentanes). Generally, asphaltenes tend to remain in solution or in colloidal suspension under reservoir temperature and pressure conditions. They may start to precipitate once the stability of the colloidal suspension is destabilized, which is caused by the changes in temperature and/or pressure during primary depletion.1 On the other hand, asphaltene have been reported to become unstable as a result of fluid blending (co-mingling) of fluid streams2 as well as by gas injection during Improved Oil Recovery (IOR) operations.3

Fast and reliable EOR process selection is a critical step in any EOR project. The digital rock (DR) approach jointly developed by Shell and SLB is aimed to be the smallest scale yet advanced EOR Pilot technology. In this document, we describe the application of DR technology for screening of different EOR mechanisms at pore-scale focused to enhance recovery from a particular reservoir formation. For EOR applications DR brings unique capabilities as it can fully describe different multiphase flow properties at different regimes. The vital part of the proposed approach is the high-efficient pore-scale simulation technology called Direct Hydrodynamics (DHD) Simulator. DHD is based on a density functional approach applied for hydrodynamics of complex systems. Currently, DHD is benchmarked against multiple analytical solutions and experimental tests and optimized for high performance (HPC) computing. It can handle many physical phenomena: multiphase compositional flows with phase transitions, different types of fluid-rock and fluid-fluid interactions with different types of fluid rheology. As an input data DHD uses 3D pore texture and composition of rocks with distributed micro-scale wetting properties and pore fluid model (PVT, rheology, diffusion coefficients, and adsorption model). In a particular case, the pore geometry comes from 3D X-ray microtomographic images of a rock sample. The fluid model is created from lab data on fluid characterization. The output contains the distribution of components, velocity and pressure fields at different stages of displacement process. Several case studies are demonstrated in this work and include comparative analysis of effectiveness of applications of different chemical EOR agents performed on digitized core samples.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractOptimum management of oil and gas reservoirs is a continuous, iterative process which encompasses monitoring the reservoir, interpreting the monitoring data, and deciding from the results how best to continue reservoir development and executing those decisions. Monitoring data vary widely in time and space scales.Temporally, they range from continuous to infrequent, episodic measurements; spatially, they range from local well-centric to global reservoir measurements.The reservoir management workflow similarly operates at multiple, parallel time-space scales. A "fast" workflow loop handles continuous well and surface network data (e.g. pressure, temperature, and rate), using fast data handling and fast decision-making to optimize hydrocarbon delivery. A "slow" workflow loop assimilates episodic reservoir data (e.g. time-lapse seismic and borehole reservoir measurements) to optimize reservoir drainage.Reservoir monitoring data are assimilated at the most appropriate time into the reservoir shared earth model, which feeds both the "fast" and "slow" workflow loops. A continuing industry challenge is to determine the best way to do this, since the types of monitoring data are diverse and the volume of data to assimilate is often vast.This paper begins with a review of reservoir monitoring data that are available today, with a focus on the range of time-space scales. A reservoir management workflow is introduced which has multiple time scales appropriate for these data. The paper concludes with a review of key challenges: (1) to develop improved interpretation technologies to unify and integrate the fast well-network centric and slow reservoir-centric workflow loops for faster conversion of measurement signals into information, and (2) to provide fuller support for uncertainties, including determining how the level of uncertainty in the reservoir model changes when assimilating monitoring data.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe proper treatment of oil recovery from vugs is an important part of evaluating a triple porosity reservoir. This paper evaluates the rate of oil recovery from isolated vugs, that is vugs that are not connected to the fracture system. Simulations of triple porosity systems are made with fine grid, single porosity and dual porosity and dual porosity models.The effect of the ratio of viscous forces to gravity forces on oil recovery from vugs is evaluated for a wide rate of ratio of the forces.A procedure is presented for creating composite curves (P cow and k row ) to be used in a dual porosity simulator to emulate oil recovery from triple porosity systems. This technique is designed for situations where no experimental displacement results are available. Discussions show that these composite curves are more reliable that the results from matching experimental displacements.Two examples of this process are shown: one at a low and another at a high value of the viscous to gravity ratio. Two realizations are matched to demonstrate how one would create average composite curves in a field situation.

Increasing demand of oil and gas worldwide is promoting a new, fast growth in the oil industry where the presence of experienced engineers is limited. Exploration for hydrocarbons is reaching limiting frontiers and the near future and long term challenge will be to maximize recovery from the existing fields. Enhanced oil recovery offers an alternative to improve recovery by means of introducing an external agent which enhances oil sweeping at a pore level scale. While the EOR concept is not new; field implementation has been scarce. As a consequence the physics governing the displacement processes have not been completely understood, posing a challenge for the design and modeling of the process. This is enhanced when dealing with numerical models, which, typically are designed for primary and/or secondary depletion processes, with grid orientations and dimensions suitable for these field conditions. Very often though, these same models are used to design and evaluate the potential for field EOR. This paper addresses the main challenges of modeling the fine scale displacement mechanism with a full field model, highlighting the typical errors in recovery efficiency that can occur and suggesting scales at which screening models can be built. Displacement processes in the reservoir are dominated by the combination of the viscous and capillary forces, the efficiency and ultimately the amount of displaced oil is controlled by the balance of these forces. During a core scale displacement process, viscous forces are dominant and most of the oil is contacted by the injected agent. This displacing mechanism is different from the one experienced at reservoir conditions where gravity forces play an important role, influencing the amount of oil which is contacted by the EOR agent, where under and overrunning may occur. Modeling of these displacements requires a greater resolution than the one used in for the full field model. The impact of model size and force balance during an EOR displacement process is presented is this paper.